This invention relates to an apparatus and a method for improving the detectability of defects occurring in a moving web. More particularly, it concerns an automatic web inspection system for detecting subtle changes in product optical qualities, identifying which of several types of defects have produced these changes, and locating these defects both in the direction of web motion and transversely thereto.
Although not restricted thereto, this defect monitoring system is particularly applicable to wide webs of nonwoven material manufactured by the process taught by Kinney in U.S. Pat. No. 3,338,992. Several different categories of defects, each identified by its own set of subtle optical characteristics, have been recognized as influencing the ultimate quality of this web material. Furthermore, since many of the defects tend to be localized in areal extent on the sheet material, it is important to be able to locate each defect in two directions, that is, both in the machine direction (MD) and cross machine direction (XD). This information enables the material to be either slit in the MD or cut in the XD to eliminate the defects and salvage the remaining acceptable material.
Many web defect measuring devices are known in the art. For example, Bossons in U.S. Pat. No. 3,803,420 teaches apparatus for detecting and classifying defects into several categories. Geis et al. in U.S. Pat. No. 3,917,414 describe a method and apparatus for monitoring the transverse position of detected defects. Similarly, Akutsu et al. in U.S. Pat. No. 3,958,128 and Nichols et al. in U.S. Pat. No. 3,898,469 teach methods for detecting defects and locating them on predetermined segments across the web material. None of the known inspection systems, however, teach apparatus which generates hypothetical elemental inspection areas (or frames) on the moving web for the purpose of improving the detectability of subtle defects. By making the frame size approximate the defect size, and discriminating each frame separately, signal-to-noise ratios are significantly improved. Furthermore, the extracted signal features from each frame can be used to classify the potential defect into one of several defect categories.